Cap Rate Decomposition: Risk-Free Rate, Risk Premium, and the Gordon Growth Build

Key Takeaways

Why NOI ÷ Value Is Not Enough

The standard one-line answer is Cap Rate = NOI ÷ Property Value. The formula is correct. It is also, on its own, of limited use for the decisions an institutional underwriter needs to make. NOI ÷ Value tells you what cap rate a deal trades at. It does not tell you whether the cap rate is defensible. It does not tell you what is in the price. It does not tell you whether 25 bps of compression between the broker's whisper and last quarter's comps reflects a tightening risk premium, a falling Treasury curve, a rising growth view, or a combination, and the three move on different timelines for different reasons.

A cap rate can also be read as a build-up. It comes from the Gordon Growth Model, applied to real estate: the value of a stream of growing income equals the income divided by the difference between required return and growth. Rearranged for cap rate, that identity readscap rate = required return − long-run NOI growth, which expands tocap rate = (risk-free rate + risk premium) − growth. NOI ÷ Value is thearithmetic output of this formula at a given price. The build-up is the structural decomposition that lets you test whether the quoted cap is rich, cheap, or fair against the fundamentals that produced it.

This article walks the build-up component by component. Each component (the 10-year Treasury baseline, the four-leg risk premium decomposition, and the long-run NOI growth term) gets a separate section with May 2026 inputs, the data sources used to size or cross-check each leg, and the practitioner judgment calls that distinguish a credible underwrite from a broker reaction. The worked example is hypothetical and resolves to a 5.88% multifamily cap; the framework generalizes to any asset class. The pillar at/learn/financial-modeling/valuation/cap-rate-calculator-and-formulacovers the ratio: what NOI is, how the formula's rearrangements work, what asset-class benchmarks look like. This article covers what is inside the ratio.

The Gordon Growth Build

The Gordon Growth Model, published by Myron Gordon and Eli Shapiro in 1956, prices a stream of growing cash flows as P = D ÷ (r − g), where P is present value, D is the next-period cash flow, r is the required return, and g is the long-run growth rate. The equity-finance version uses dividends; the real estate version uses NOI. Substituting:

Value = NOI ÷ (r − g)

Cap rate is defined as NOI ÷ Value. Rearranging the Gordon Growth equation gives:

Cap Rate = NOI ÷ Value = r − g

That single identity is the backbone of the framework. The cap rate equals the required return on the asset, less the long-run growth in NOI. Both terms are estimates, both are negotiable, and both move on different timelines. Strictly, the identity holds for forward NOI after capital reserves and a constant growth rate; the build-up here uses NOI as the cash flow for simplicity.

The required return r itself decomposes into a risk-free rate plus a risk premium:r = r_f + risk premium. This parallels the Capital Asset Pricing Model (r = r_f + β × ERP) used in equity finance, but CRE practitioners don't estimate beta. The cross-section of comparable transactions gives a more direct read on the risk premium than regression against a market index would, since there is no continuously priced market portfolio for private real estate. Aswath Damodaran publishes equity and country risk premium data (pages.stern.nyu.edu/~adamodar) for the CAPM construction; the real estate build-up replaces the beta-times-ERP term with a premium sized from the four legs below. One wrinkle: his current convention nets the U.S. default spread out of the Treasury yield to get a dollar risk-free rate. This article uses the nominal 10-year Treasury yield directly, the usual real estate shorthand, so every premium here is measured over the 10-year.

Putting the two pieces together gives the build-up:

Cap Rate = r_f + Risk Premium − Growth

Three legs. Each leg has its own driver, data source, and update frequency. The 10-year Treasury moves daily and in stress periods can move sharply within a month. Risk premiums adjust over quarters. Growth assumptions adjust over years. The dispersion across asset classes sits mostly in the second leg, and the disagreement between bulls and bears on any single sector tends to live in the third.

Component 1: Risk-Free Rate

The risk-free rate is the yield on a U.S. Treasury security. In institutional CRE practice, the 10-year Treasury is a common benchmark for the going-in cap rate of a stabilized asset. TheFederal Reserve Economic Data series DGS10 publishes the constant-maturity 10-year yield daily, which makes it a convenient anchor for house-view cap rate decks. During May 2026 the series closed between 4.36% and 4.67%, a 31 bp range within the month; the worked example below uses the 4.38% close of May 8, 2026, and the 4.56% close of May 22, 2026 appears as a sensitivity.

Why 10-year, not 30-year

Three reasons drive the 10-year convention. First, for an assumed hold of 7–10 years on a stabilized asset, the duration match is closer to the 10-year than the 30-year. Second, the 10-year is a highly liquid point on the curve, so it is a low-noise reference. Third, many permanent loans are priced as a spread over a benchmark such as the 10-year Treasury, so a 10-year-anchored cap rate keeps the equity and debt cost-of-capital frameworks in the same units.

The 30-year is sometimes used as a duration-extension proxy when underwriting truly long-duration cash flows, such as ground leases with 99-year terms or certain pension-fund mandates, but it is not the usual default. For most underwriting the 10-year is the reference.

Nominal, not real

The Treasury yield used is the nominal yield, not the TIPS-derived real yield. The reason is that the cap rate is a nominal yield on a nominal NOI; the growth term in the build-up is nominal NOI growth, not real. Using the real Treasury would force you to deflate NOI growth, which works in theory but loses the practitioner's direct connection to comparable mortgage rates and broker quotes. The usual practice is to keep every input nominal: the required return carries expected inflation, and the growth term carries the inflation embedded in rents and expenses. Neither side is deflated.

Spot, not forward

The risk-free leg uses the spot 10-year, not a forward curve. Forward curves embed market expectations about rate paths that can prove wrong, and using them adds a forecasting layer most underwriters can't defend at IC. The discipline is: use today's 10-year as the baseline, then sensitize the underwrite to ±100 bps on the rate input as a separate scenario. Don't bake a rate forecast into the going-in cap.

Component 2: Risk Premium

The risk premium is the spread over the risk-free rate that compensates the equity investor for taking on the risks unique to commercial real estate. In the hypothetical May 2026 example, institutional multifamily core carries a risk premium of 350 bps over the 10-year; after subtracting 2.00% of growth, the resulting cap sits 150 bps over the 10-year. Assets with more volatile or less certain cash flow, such as suburban office and full-service hospitality, carry larger premiums. The magnitudes in this article are illustrative assumptions, not published series. The dispersion across property types is not arbitrary: it decomposes into four sub-components, each driven by a different risk factor and each sized against a different market reference where one exists.

Sub-component 2a: Credit risk of the tenant base

The credit-risk leg compensates for the risk of NOI interruption from tenant default or non-renewal. For single-tenant net lease (STNL) assets, this leg is largest in absolute terms: an asset leased to a BBB-rated investment-grade tenant on a 15-year triple-net lease has credit risk priced directly off corporate bond spreads. The ICE BofA BBB US Corporate Index option-adjusted spread, published on FRED (BAMLC0A4CBBB), ranged from 0.93% to 1.16% across daily readings in Q1 2026, or roughly 93–116 bps.

For multifamily and diversified-tenant assets, credit risk decomposes differently. Multifamily has hundreds of short-lease tenants whose idiosyncratic default risk diversifies at the portfolio level. The expected loss from that risk sits in NOI as a bad-debt line; adding it again as a yield premium would double count it. The credit leg instead compensates for unexpected loss, the chance that losses run well above the underwritten bad-debt line in a downturn. For institutional multifamily core that cushion is modest (75 bps in the example; an illustrative range is 50–100 bps). Office and retail with concentrated rent rolls fall between multifamily and single-tenant net lease, with the credit premium scaling roughly with the concentration of the top five tenants.

Sub-component 2b: Illiquidity premium

Private real estate is illiquid relative to listed REITs that own similar properties, and a private buyer needs to be paid for that. No published series isolates this leg, so it is an analyst assumption (150 bps in the example). The cross-check comes from public markets. Green Street'sCommercial Property Price Index is built on REIT-owned asset valuations, delivers monthly updates, and is value-weighted, so you can compare REIT-implied values for institutional-quality property with private-market pricing.

Read the direction of that cross-check carefully. When private pricing is tighter than REIT-implied pricing, the private market is accepting a lower yield than the public market, which is the opposite of being paid for illiquidity. That gap is a warning that the assumed illiquidity premium may be too high for current private pricing, not an input to add on top. The illiquidity premium is the part of the risk premium that compensates for the inability to rebalance the position in a single trading day, and it is the largest and least observable of the four legs.

Sub-component 2c: Structural premium

The structural premium captures asset-class-level basis risk that doesn't fit into the credit or illiquidity buckets. Examples: a ground-leased asset trades wider than a fee-simple asset because the ground-lease term eventually resets and embeds reversion risk; a subordinated condominium interest trades wider than a wholly-owned asset because the governance structure can constrain operating decisions; an asset in a flood zone or seismic zone trades wider to compensate for tail event risk that doesn't show up in operating expenses.

For typical institutional multifamily core on a fee-simple, unencumbered basis, the structural premium is smallest (75 bps in the example; an illustrative range is 50–100 bps). For a ground-leased asset with 50 years remaining, an illustrative range is 150–250 bps. For a subordinated interest, it can be 200–400 bps. Along with the sponsor premium, the structural premium is one of the two legs that vary most deal-to-deal within a single asset class, and it is the leg most often missed by analysts pricing off a broad asset-class comp set.

Sub-component 2d: Sponsor / operator premium

The sponsor premium compensates the buyer for risks specific to the operating sponsor: track record, deal-execution capability, alignment of interests, geographic depth, and reporting infrastructure. The example sets a baseline of 50 bps for an institutional sponsor with a long, cycle-tested track record. On the author's illustrative scale (not an industry standard), a regional sponsor with a shorter track record carries 75–125 bps, and a first-time or non-institutional sponsor carries 100–200 bps, so the same physical asset can trade 50–150 bps wider with the weaker sponsor. A sponsor with a recent fund underperformance may trade wider still, even when the physical risk profile is identical.

The sponsor premium is the leg most often disputed during deal negotiation. Buyers want it sized to the transaction-level risk; sponsors argue it should be sized to the franchise-level risk. The discipline is to size the sponsor premium against the spread observed in repeat transactions for the same sponsor relative to asset-class median, not against a generic narrative about “Class A operators.”

Putting the four legs together

For the hypothetical institutional multifamily core deal in May 2026, the four sub-components are sized as follows:

Table 1: The multifamily core risk premium, decomposed. All four sizings are illustrative assumptions. The structural and sponsor legs vary most deal-to-deal; the credit and illiquidity legs have market cross-checks (corporate spreads and REIT-implied pricing).

Component 3: Long-Run NOI Growth

The growth term g in the Gordon Growth Model is the long-run growth rate of NOI, measured in nominal dollars, net of expense growth. This is the most misunderstood leg of the build-up, not because the concept is difficult, but because many practitioners conflate revenue growth with NOI growth, and the conflation distorts the cap-rate build.

Revenue growth is not NOI growth

Suppose a multifamily property in Phoenix has long-run nominal rent growth of 3.0% per year (an illustrative assumption for a Sun Belt metro). That 3.0% is gross potential rent growth. Now suppose operating expenses grow at 3.5% per year, faster than rents, a common underwriting assumption because insurance, property taxes, and labor have been compounding above the rent index in many markets.

With expenses at roughly 45% of effective gross income, the first-year NOI growth rate is:

g_NOI = 3.0% × (1 / 0.55) − 3.5% × (0.45 / 0.55) = 5.45% − 2.86% = 2.59%

So the Phoenix sample implies about 2.59% year-one NOI growth, 41 bps below the 3.0% rent growth. That gap is the error from treating revenue growth as NOI growth. It is also a year-one figure. The Gordon Growth Model assumes g is a perpetual rate, and in this example expenses grow faster than revenue, so the expense share rises every year and NOI growth fades below 2.59% over time. A perpetual 3.0% rent growth would also mean rents outrun inflation forever.

From year-one growth to a long-run rate

In practice, underwriters set the perpetual growth rate by anchoring to expected inflation and then adjusting for the expense drag. For most stabilized institutional CRE, real NOI growth in the long run is close to zero: rents track inflation, expenses track inflation slightly faster, and real NOI is roughly flat or modestly negative. The long-run nominal NOI growth term therefore sits at or below expected inflation.

The 10-year breakeven inflation rate (the spread between nominal 10Y and 10Y TIPS;FRED T10YIE) ranged from 2.38% to 2.50% across May 2026 and was 2.40% on May 22. With long-run NOI growth set roughly 40–50 bps below breakeven to reflect expense drag, the long-run nominal NOI growth term comes in at about 2.00% for stabilized multifamily core. That number sits below both the headline 3% rent growth most underwriters quote and the 2.59% year-one Phoenix figure, because it nets out the expense drag and the fade.

Why the example uses 2.00%

The example subtracts a nominal long-run NOI growth rate of 2.00% from a nominal required return. Because both sides are nominal, nothing else is adjusted: the inflation in the 10-year Treasury sits inside r, and the inflation in rents and expenses sits inside g. The 2.00% is an illustrative assumption built from the breakeven-and-drag reasoning above.

Cap rate = 7.88% (required return) − 2.00% (nominal NOI growth) = 5.88%

This leg moves the cap one-for-one, so errors are costly. Subtracting the 2.59% year-one Phoenix figure instead of 2.00% understates the cap by 59 bps; subtracting the 3.00% rent growth understates it by 100 bps; subtracting nothing overstates it by 200 bps.

The negative-growth edge case

For sectors where nominal NOI is expected to contract, such as suburban office post-2020 or large-format unanchored retail in oversupplied markets, the growth term flips sign. Take a suburban office asset whose nominal NOI is expected to shrink 1.5% a year, with an illustrative 4.50% risk premium: it builds as 4.38% UST + 4.50% risk premium + 1.50% (negative growth becomes a positive addition) = 10.38% cap. The required return of 8.88% is only 100 bps above the multifamily example's 7.88%, but the cap is 450 bps wider, because 350 bps of the gap comes from growth flipping from +2.00% to −1.50%. The build-up explains the wider cap directly; a generic risk-premium-only model would miss the growth swing.

Worked Example: A May 2026 Multifamily Core Cap, Built from Components

Consider a hypothetical stabilized 240-unit Class B+ garden-style multifamily property in Tampa, marketed for institutional sale in May 2026. The broker's whisper number is a 5.85% cap; the offering memorandum cites sub-market comps in the 5.75–6.00% range. The asset, the whisper, and the comps are all hypothetical, and the premium inputs are national illustrative assumptions that a real underwrite would refine with a submarket comp set. The question for the underwriter is not "is 5.85% the right number" (the market will decide that in a competitive bid) but rather "what does 5.85% imply about the components, and are those components defensible against fundamentals?"

Walk the build-up:

Table 2: Hypothetical Tampa multifamily core build-up, May 2026. The broker quote of 5.85% sits 3 bps inside the build-up, well within practitioner-judgment tolerance.

The build-up reads 5.88% cap. The broker whisper reads 5.85%. The gap is 3 basis points, a rounding error against the practitioner-judgment uncertainty in any single leg. On these inputs the deal is fair-priced against fundamentals. There is no obvious story being paid for, and no obvious mispricing the underwriter is leaving on the table. The input date matters, though: on the 4.56% close of May 22, 2026, the same build gives 4.56% + 3.50% − 2.00% = 6.06%, which would put the 5.85% whisper 21 bps inside the build-up.

If the broker whisper had instead been 5.25%, 63 bps inside the build-up, the underwriter has to find some combination of three things to justify the tighter cap: (1) nominal NOI growth about 63 bps above the 2.00% base case, or roughly 2.63%, supported by submarket rent growth that is materially above the metro index; (2) lower structural and sponsor premiums, which total 125 bps in the build-up, so closing 63 bps means cutting them roughly in half, which requires identifying why this asset's basis risk is unusually low and why this sponsor deserves a clear discount to the institutional baseline; or (3) an illiquidity premium well below the 150 bps assumed, which needs support from the REIT-implied cross-check. If none of those justifications hold, the cap is rich and the deal should be passed unless the buyer has a strategic reason to stretch.

The same logic runs in the other direction. If the broker whisper had been 6.50%, 62 bps wider than the build-up, the underwriter should ask: what is the market seeing that my build-up doesn't capture? Either the sponsor premium has been mispriced wider (recent fund underperformance, key-person risk), the structural premium is higher than I scored it (deferred maintenance, capital event approaching), or the growth assumption embedded in the comp set is materially below my 2.00%. Any of those should be investigated before submitting a bid.

This is the discipline the build-up enforces. Rather than reacting to a broker quote, the underwriter is forced to attribute the quote to specific components and test each one against external references. The build-up is decision-support, not a price-setting algorithm. The pocket model that runs this build-up arithmetic for institutional multifamily core isAQ-110; for anchored retail it is AQ-301; for industrial it is AQ-401.

The Decomposition's Three Practitioner Uses

The build-up framework is decision-support across three institutional workflows. The first is the most common; the third is the most analytically valuable.

Use 1: Gut-check a broker's market cap

Build the cap from components using the current 10-year and an asset-class-appropriate risk premium. Compare to the broker quote. If the gap is more than 25 bps, attribute it to a specific component: either the broker is pricing a growth or sponsor premium your build-up isn't, or the broker quote is off. Doing it formally with a written attribution is what distinguishes IC-ready underwriting from broker reaction.

Use 2: Size the sponsor-specific premium for a non-institutional operator

For deals with first-time fund sponsors or other non-institutional operators, the sponsor premium cannot be looked up; it has to be estimated. The build-up gives you a quantitative anchor: take the risk premium for the asset class (e.g., 350 bps for the multifamily core example), add an incremental sponsor premium (an illustrative 50–150 bps for non-institutional operators, for a 400–500 bps total), and compare the resulting required return to recent transactions the same sponsor has executed. If the sponsor's recent transactions consistently trade 75 bps wide of the institutional median, the incremental sponsor premium is 75 bps. The build-up converts an intuition ("the sponsor is non-institutional") into a sized adjustment that can be defended at IC.

Use 3: Align debt and equity cost-of-capital so going-in, exit, and underwritten IRR are internally consistent

This is the highest-leverage use of the framework. The going-in cap, the exit cap, the underwritten IRR, and the loan constant should all sit on the same risk curve. The build-up makes the alignment explicit:

• The going-in cap uses the spot 10-year + current risk premium − current growth view.
• The exit cap runs the same build-up using today's spot 10-year (not a forward curve), the risk premium for the asset as it will look at exit, and the growth view at exit. Because exit is years away, many underwriters add an explicit spread (for example 25–50 bps) as a stated buffer against forecast uncertainty rather than hiding a rate forecast in the input.
• The underwritten levered IRR should sit above the unlevered required return (the 4.38 + 3.50 = 7.88% in the worked example) when leverage is positive. The size of the uplift depends on the leverage and the loan constant, so size it deal by deal rather than with a rule of thumb.
• The loan constant (debt service / loan amount) should sit below the going-in cap for positive leverage to work; the spread between the going-in cap and the loan constant is the levered yield pick-up on day one.

When these four numbers are internally inconsistent (the underwriter projects 8% IRR from a 5.25% going-in cap with positive leverage and a flat exit), the build-up exposes which input is the inconsistency. Often it's the implicit growth assumption in the exit cap that's doing the work, even though the underwriter didn't realize they were taking that view. The build-up surfaces the assumption explicitly so it can be debated at IC.

Cap Rate vs DCF Discount Rate: The Equivalence

The Gordon Growth identity makes the relationship between cap rate and DCF discount rate explicit. Rearranging:

r = cap rate + g

The DCF discount rate equals the cap rate plus the long-run NOI growth term. They are not different concepts; they are different points on the same equation. The cap rate is the implied discount rate for a no-growth perpetuity; the DCF discount rate is the cap rate with the growth term added back.

In the worked example: cap rate 5.88% + nominal NOI growth 2.00% = required return 7.88%. The 7.88% is the unlevered required return that should drop into a DCF as the discount rate, the cost of capital for the asset's unlevered cash flows. The cap rate of 5.88% is what you observe in the market; the discount rate of 7.88% is what you should use to discount projected cash flows in a multi-year DCF model whose NOI grows about 2.00% a year. The difference is large: on a growing perpetuity, discounting at the 5.88% cap rate instead of 7.88% would value NOI at about 25.8 times instead of 17.0 times, roughly 50% too high.

This equivalence has a practical consequence: if your DCF discount rate and your direct-cap going-in cap don't reconcile through the growth term, one of them is wrong. A common error in DCFs is using a discount rate that's not built up from the same framework as the cap rate, producing a valuation that's internally inconsistent. A DCF that uses an 8.5% discount rate alongside a 5.25% going-in cap is implicitly assuming 3.25% long-run nominal NOI growth, which is 125 bps above the 2.00% assumed in the worked example. That assumption may be defensible; it may not. But it should be debated explicitly, not buried inside a discount-rate input cell.

The article at/learn/financial-modeling/valuation/dcf-vs-direct-capitalization-when-to-useextends this comparison into the choice between the two methodologies for any given deal. When growth, reserves, and risk are modeled consistently, the two methods reconcile; when they diverge, the build-up tells you which assumption is driving the divergence.

Historical Risk-Premium Tracking

The discipline that makes the build-up useful over time is tracking the premium: the running estimate of how wide cap rates trade over the 10-year Treasury for each asset class. Two quantities matter, and they are not the same. The cap spread is the cap rate less the 10-year yield. Because cap rate = r_f + risk premium − g, the cap spread equals the risk premium less growth. Adding growth back gives the implied risk premium: implied risk premium = cap spread + g. In the worked example the cap spread is 150 bps (5.88% less 4.38%) and the implied risk premium is 350 bps (150 + 200).

To track it, pair a quarterly cap rate series for the asset class with the DGS10 close on the same date, compute the cap spread, and add your growth assumption. The same identity works in changes:change in cap = change in r_f + change in risk premium − change in g. The first term comes straight from FRED. For reference, DGS10 closed at:

Table 3: 10-year Treasury constant-maturity yield on selected dates (FRED DGS10). The risk-free leg rose 306 bps from year-end 2021 to year-end 2024 and eased 20 bps from year-end 2024 to May 8, 2026.

Here is how the attribution works, using hypothetical cap rates to show the mechanics. Suppose a multifamily cap series moved from 4.0% to 6.0% between year-end 2021 and year-end 2024, a 200 bp widening. The risk-free leg alone rose 306 bps. If growth was unchanged, that leaves a risk-premium change of −106 bps: the cap widened by less than the Treasury, which means the premium compressed. With real series from NCREIF or MSCI Real Capital Analytics in place of the hypothetical caps, the same three lines of arithmetic answer the question "is this cycle different?" for any asset class. Whether a move in the premium is cyclical or structural is a separate judgment, but the decomposition at least isolates it from the rate move.

The decomposition also supports scenario work. If the 10-year fell from 4.38% back to 3.50% and the risk premium and growth terms did not change, the example cap would fall 88 bps, from 5.88% to 5.00%. Any cap-rate move beyond the rate move is a statement about the premium or growth, and it should be argued as one.

Three data sources are common inputs for the cap series. The NCREIF Property Index is a quarterly, value-weighted composite of properties held on behalf of tax-exempt institutions. MSCI Real Capital Analytics publishes transaction-based pricing. Green Street's CPPI is built on REIT-owned asset valuations and, per Green Street, reflects live portfolio values rather than closed transactions or formal appraisals. Each methodology produces a slightly different number, and the three are most useful in combination.

How to Model the Build-Up in Excel

The cap-rate decomposition fits on a single Excel tab. Six rows, in this order, each with an input cell and a source comment:

• 1. Risk-free rate (10-year Treasury). Pull from FRED DGS10 on the date of pricing (the example uses the May 8, 2026 close of 4.38%). Update before each IC submission, because this number moves daily.

• 2. Credit risk premium. For multifamily and diversified-tenant assets, size an unexpected-loss cushion (75 bps in the example; an illustrative 50–100 bps). Expected credit loss belongs in NOI, not in this row. For STNL, size off the corporate bond spread for the tenant's credit rating; the BBB spread ran roughly 93–116 bps over Treasury in Q1 2026 (FRED BAMLC0A4CBBB). For unrated tenants there is no bond spread to read, so size the premium from the tenant's financial strength and the lease's NNN protections.

• 3. Illiquidity premium. An analyst assumption (150 bps in the example). Cross-check it against REIT-implied pricing such as the Green Street CPPI, remembering that private pricing tighter than REIT-implied pricing argues for a smaller premium, not a larger one. For special-purpose assets (data centers, self-storage when the listed REIT comp set is thin), use sector-specific judgment.

• 4. Structural premium. Score the asset against the asset-class median. Illustrative ranges: fee-simple, unencumbered, no flood zone, no seismic, 50–100 bps (75 in the example); ground lease with 50+ years remaining, 150–250 bps; subordinated interest, 200–400 bps; flood zone or seismic zone, add 50–150 bps.

• 5. Sponsor premium. Use your own tiering and say so; there is no industry-standard scale. On the illustrative scale in this article: institutional sponsor with a long, cycle-tested track record, 50 bps (the baseline); regional sponsor with a shorter track record, 75–125 bps; first-time sponsor or non-institutional operator, 100–200 bps. Calibrate against repeat transactions by the same sponsor.

• 6. Long-run nominal NOI growth. For stabilized institutional CRE, the example uses 2.00%, about 40–50 bps below 10-year breakeven inflation. For growth-asset classes (data centers, life sciences in supply-constrained markets), a higher rate, for example 2.50–3.00%. For declining-NOI sectors (suburban office, secondary retail), a negative rate, for example −1.0% to −3.0%. All of these are illustrative.

The output cell is row 7: Cap Rate = Row 1 + Row 2 + Row 3 + Row 4 + Row 5 − Row 6. Compare against the broker quote, recent transaction comps, and the asset-class median from NCREIF or MSCI RCA. If the gap is more than 25 bps, attribute it to one of the six rows and document why. This single tab is the discipline that distinguishes a defensible underwrite from a price-taker.

Common Mistakes

Seven errors that distort cap-rate build-ups in practice:

• Subtracting the wrong growth rate. The growth term in the build-up is long-run nominal NOI growth, not the headline rent-growth number and not zero. In the worked example, subtracting 3.00% rent growth instead of 2.00% NOI growth understates the cap rate by 100 bps and makes every deal look cheap; subtracting nothing overstates it by 200 bps and makes every deal look rich.

• Using a forward Treasury curve instead of spot. Forward rates embed market expectations about policy paths that can prove wrong. The discipline is to use spot 10-year and sensitize the underwrite to ±100 bps as a separate scenario, not to bake a forecast into the going-in cap.

• Treating the risk premium as a single number rather than four sub-components. Quoting "350 bps risk premium" without decomposing into credit, illiquidity, structural, and sponsor obscures which leg is moving. A widening risk premium that's all illiquidity is a different signal from one that's all sponsor: the first is tied to market-wide liquidity conditions, the second to the specific operator.

• Pricing the sponsor premium off a narrative rather than transaction data. "Class A sponsor" is not a sized adjustment. The sponsor premium should be calibrated against the spread observed in repeat transactions for the same sponsor relative to asset-class median. If the data doesn't support a tighter sponsor premium, the narrative shouldn't drive the build-up.

• Using a national risk premium for a non-gateway market. National indices are not market-specific: the NCREIF Property Index, for example, is value-weighted, so larger-value markets weigh more heavily. A submarket in a smaller metro can trade materially wide of the national average for the same asset class, mostly through a wider sponsor premium and a wider illiquidity premium. The decomposition should reference the submarket-specific comp set, not the national index.

• Letting the going-in cap and the exit cap live in different frameworks. If the going-in cap is built up from components and the exit cap is "going-in + 25 bps" by convention, the exit cap isn't sized to the exit-date risk premium or exit-date growth view. The convention may be right by accident; it's not right by construction. The exit cap should be built up from the same components, with the exit-date assumptions stated explicitly and any conservatism added as a visible line.

• Conflating cap rate with discount rate. The cap rate is the discount rate for a no-growth perpetuity; the DCF discount rate is the cap rate plus the growth term. Using the same number for both in a multi-year DCF understates the discount rate by the full growth term (200 bps in the worked example) and overstates the present value of the cash flows. Either build both numbers explicitly or reconcile the implicit assumption.

Related Articles

The build-up framework spokes off the cap rate pillar. Related deep-dives in the valuation cluster:

• Cap Rate Calculator and Formula: the pillar. NOI ÷ Value, the formula's three rearrangements, asset-class benchmarks, the calculator widget.
• Going-In Cap vs. Exit Cap: Modeling the Spread: the exit-leg of the build-up, with the spread mechanics and the structural-conservatism conventions.
• DCF vs. Direct Capitalization: When to Use Which: the methodological choice between cap-rate-based and discount-rate-based valuation, with the equivalence math from this article extended.
• Terminal Value, Reversion, and Exit Cap Across the Hold Period: the Gordon Growth applied to the terminal year of a DCF model.
• NOI: Institutional vs. Broker Calculation: what counts as NOI in the cap rate formula; the definitional differences that swing the build-up by 25–50 bps.
• Replacement Cost Analysis: Development and Insurance Frameworks: the cost-side cousin of the cap-rate build-up, for ground-up and casualty-loss contexts.

FAQ

Sources

• Federal Reserve Economic Data, 10-Year Treasury Constant Maturity Rate (DGS10): daily 10-year Treasury yield series; the risk-free leg of the build-up.
• Federal Reserve Economic Data, 10-Year Breakeven Inflation Rate (T10YIE): market-implied expected inflation over 10 years; the anchor for the growth assumption.
• Federal Reserve Economic Data, ICE BofA BBB US Corporate Index Option-Adjusted Spread (BAMLC0A4CBBB): BBB corporate spread to Treasury; the credit-leg reference for single-tenant net lease.
• Aswath Damodaran, NYU Stern, Country Default Spreads and Risk Premiums: risk premium data and the U.S. dollar risk-free rate convention; last updated January 5, 2026.
• Green Street Commercial Property Price Index (CPPI): monthly, value-weighted index built on REIT-owned asset valuations; the public-market cross-check for the illiquidity leg.
• NCREIF Property Index (NPI): quarterly, value-weighted composite of operating properties held on behalf of tax-exempt institutions.
• Gordon, M.J. and Shapiro, E. (1956), "Capital Equipment Analysis: The Required Rate of Profit," Management Science, 3(1), 102–110: original publication of the Gordon Growth Model.